Literature DB >> 31520402

Diagnostic value of the International Ovarian Tumor Analysis (IOTA) simple rules versus pattern recognition to differentiate between malignant and benign ovarian masses.

Dina M R Dakhly1, Hassan M Gaafar1, Mona M Sediek1, Mohamed F Ibrahim2, Mohamed Momtaz1.   

Abstract

OBJECTIVE: To compare the efficacy of the International Ovarian Tumor Analysis (IOTA) simple rules versus pattern recognition to differentiate between benign and malignant ovarian masses.
METHODS: A prospective cross-sectional study conducted at Kasr El Aini Hospital, Cairo, between April 2016 and October 2018 of 396 women with ovarian masses measuring more than 5 cm who were candidates for surgery. All patients underwent two-dimensional transvaginal sonography: level 2 with IOTA simple rules followed by level 3 with pattern recognition. Patients subsequently underwent ovarian cystectomy or oophorectomy and the specimens were examined histopathologically. Accuracy was measured by comparing sensitivity, specificity, positive predictive value, negative predictive value, and accuracy.
RESULTS: IOTA simple rules specified 44/50 cases as malignant and 220/242 as benign (sensitivity and specificity of 88.0% and 90.9%, respectively). Pattern recognition identified 83/94 cases as malignant and 281/302 as benign (sensitivity and specificity of 88.3% and 92.7%, respectively). No statistically significant difference in accuracy was found between the two methods.
CONCLUSION: IOTA simple rules are an effective tool for detecting ovarian malignancy when performed by nonexpert sonographers. When results are inconclusive, pattern recognition should be performed additionally by an expert sonographer. CLINICAL TRIALS REGISTRATION: NCT02800031.
© 2019 International Federation of Gynecology and Obstetrics.

Entities:  

Keywords:  Benign ovarian mass; IOTA simple rules; Level 2 ultrasound; Level 3 ultrasound; Ovarian malignancy; Pattern recognition

Year:  2019        PMID: 31520402     DOI: 10.1002/ijgo.12970

Source DB:  PubMed          Journal:  Int J Gynaecol Obstet        ISSN: 0020-7292            Impact factor:   3.561


  4 in total

1.  A machine learning approach applied to gynecological ultrasound to predict progression-free survival in ovarian cancer patients.

Authors:  Francesca Arezzo; Gennaro Cormio; Daniele La Forgia; Carla Mariaflavia Santarsiero; Michele Mongelli; Claudio Lombardi; Gerardo Cazzato; Ettore Cicinelli; Vera Loizzi
Journal:  Arch Gynecol Obstet       Date:  2022-05-09       Impact factor: 2.493

2.  Predicting Malignancy in Adnexal Masses by the International Ovarian Tumor Analysis-Simple Rules.

Authors:  Vrushti Solanki; Pratibha Singh; Charu Sharma; Navdeep Ghuman; Binit Sureka; Shashank Shekhar; Meenakshi Gothwal; Garima Yadav
Journal:  J Midlife Health       Date:  2021-01-21

3.  A comparison of the diagnostic performance of the O-RADS, RMI4, IOTA LR2, and IOTA SR systems by senior and junior doctors.

Authors:  Yuyang Guo; Baihua Zhao; Shan Zhou; Lieming Wen; Jieyu Liu; Yaqian Fu; Fang Xu; Minghui Liu
Journal:  Ultrasonography       Date:  2022-01-31

Review 4.  Radiomics and radiogenomics in ovarian cancer: a literature review.

Authors:  S Nougaret; Cathal McCague; Hichem Tibermacine; Hebert Alberto Vargas; Stefania Rizzo; E Sala
Journal:  Abdom Radiol (NY)       Date:  2020-11-11
  4 in total

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